We show experimentally that the algorithm clarans of Ng and Han (1994) finds better K -medoids solutions than the V oronoi iteration algorithm of Hastie et al. (2001). This finding, along with the similarity between the V oronoi iteration algorithm and Lloyd's K -means algorithm, motivates us to use clarans as a K -means initializer. We show that clarans outperforms other algorithms on 23/23 datasets with a mean decrease over k-means-++ (Arthur and V assilvitskii, 2007) of 30% for initialization mean squared error (MSE) and 3% for final MSE. We introduce algorithmic improvements to clarans which improve its complexity and runtime, making it a viable initialization scheme for large datasets.

We show experimentally that the algorithm clarans of Ng and Han (1994) finds better K-medoids solutions than the Voronoi iteration algorithm of Hastie et al. (2001). This finding, along with the similarity between the Voronoi iteration algorithm and Lloyd's K-means algorithm, motivates us to use clarans as a K-means initializer. We show that clarans outperforms other algorithms on 23/23 datasets with a mean decrease over k-means-++ (Arthur and Vassilvitskii, 2007) of 30% for initialization mean squared error (MSE) and 3% for final MSE. We introduce algorithmic improvements to clarans which improve its complexity and runtime, making it a viable initialization scheme for large datasets.

Clustering is one of the major techniques used for statistical data analysis. As the term suggests, "clustering" is defined as the process of gathering similar objects into different groups or distribution of datasets into subsets with a defined distance measure. K-means clustering is touted as a foundational algorithm every data scientist ought to have in their toolbox. K-means and k-medoids are methods used in partitional clustering algorithms whose functionality works based on specifying an initial number of groups or, more precisely, iteratively by reallocation of objects among groups. The algorithm works by first segregating all the points into an already selected number of clusters.

Clustering non-Euclidean data is difficult, and one of the most used algorithms besides hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also simply referred to as k-medoids clustering. In Euclidean geometry the mean-as used in k-means-is a good estimator for the cluster center, but this does not exist for arbitrary dissimilarities. PAM uses the medoid instead, the object with the smallest dissimilarity to all others in the cluster. This notion of centrality can be used with any (dis-)similarity, and thus is of high relevance to many domains and applications. A key issue with PAM is its high run time cost. We propose modifications to the PAM algorithm that achieve an O(k)-fold speedup in the second ("SWAP") phase of the algorithm, but will still find the same results as the original PAM algorithm. If we relax the choice of swaps performed (while retaining comparable quality), we can further accelerate the algorithm by eagerly performing additional swaps in each iteration. With the substantially faster SWAP, we can now explore faster initialization strategies, because (i) the classic ("BUILD") initialization now becomes the bottleneck, and (ii) our swap is fast enough to compensate for worse starting conditions. We also show how the CLARA and CLARANS algorithms benefit from the proposed modifications. While we do not study the parallelization of our approach in this work, it can easily be combined with earlier approaches to use PAM and CLARA on big data (some of which use PAM as a subroutine, hence can immediately benefit from these improvements), where the performance with high k becomes increasingly important. In experiments on real data with k=100,200, we observed a 458x respectively 1191x speedup compared to the original PAM SWAP algorithm, making PAM applicable to larger data sets, and in particular to higher k.

We show experimentally that the algorithm CLARANS of Ng and Han (1994) finds better K-medoids solutions than the Voronoi iteration algorithm of Hastie et al. (2001). This finding, along with the similarity between the Voronoi iteration algorithm and Lloyd's K-means algorithm, motivates us to use CLARANS as a K-means initializer. We show that CLARANS outperforms other algorithms on 23/23 datasets with a mean decrease over k-means of 30% for initialization mean squared error (MSE) and 3% for final MSE. We introduce algorithmic improvements to CLARANS which improve its complexity and runtime, making it a viable initialization scheme for large datasets. Papers published at the Neural Information Processing Systems Conference.

This artificial intelligence program, named ClaRAN, has the ability to scan images taken by radio telescopes. With the responsibility to identify radio galaxies, galaxies that emit powerful radio jets from supermassive black holes at their centres, ClaRAN is the brainchild of big data specialist Dr Chen Wu and astronomer Dr Ivy Wong, both from The University of Western Australia in partnership with the International Centre for Radio Astronomy Research (ICRAR). Wong explains: "These supermassive black holes occasionally burp out jets that can be seen with a radio telescope." "Over time, the jets can stretch a long way from their host galaxies, making it difficult for traditional computer programs to figure out where the galaxy is." "That's what we're trying to teach ClaRAN to do." Describing the origin of the artificial intelligence program, Dr Wu discusses how ClaRAN grew out of an open source version of Microsoft and Facebook's object detection software. The program was completely overhauled and trained to recognise galaxies instead of people.

Researchers have taught an artificial intelligence program used to recognise faces on Facebook to identify galaxies in deep space. The result is an AI bot named ClaRAN that scans images taken by radio telescopes. Its job is to spot radio galaxies - galaxies that emit powerful radio jets from supermassive black holes at their centres. ClaRAN is the brainchild of big data specialist Dr Chen Wu and astronomer Dr Ivy Wong, both from The University of Western Australia node of the International Centre for Radio Astronomy Research (ICRAR). Dr Wong said black holes are found at the centre of most, if not all, galaxies.

An artificial intelligence (AI) programme used to recognize faces on Facebook can also identify galaxies in deep space, scientists said Wednesday. The AI bot named ClaRAN scans images taken by radio telescopes, said researchers from the International Centre for Radio Astronomy Research (ICRAR) in Australia. Its job is to spot radio galaxies -- galaxies that emit powerful radio jets from supermassive black holes at their centers, according to the research published in the journal Monthly Notices of the Royal Astronomical Society. Black holes are found at the center of most, if not all, galaxies. "These supermassive black holes occasionally burp out jets that can be seen with a radio telescope," said Ivy Wong from The University of Western Australia node of the International Centre for Radio Astronomy Research (ICRAR).

Researchers have taught an artificial intelligence program used to recognise faces on Facebook to identify galaxies in deep space. The result is an AI bot named ClaRAN that scans images taken by radio telescopes. Its job is to spot radio galaxies--galaxies that emit powerful radio jets from supermassive black holes at their centres. ClaRAN is the brainchild of big data specialist Dr. Chen Wu and astronomer Dr. Ivy Wong, both from The University of Western Australia node of the International Centre for Radio Astronomy Research (ICRAR). Dr. Wong said black holes are found at the centre of most, if not all, galaxies.

Astronomers have trained Facebook's facial recognition software to spot'burping' black holes in deep space. The artificial intelligence (AI) tool is programmed to pick out radio galaxies out from scans taken by radio telescopes. These rare galaxies spew powerful radio jets from the supermassive black holes at their centres, and scientists believe they hold clues to the structure of the universe. Using the new programme, dubbed ClaRAN, experts at the University of Western Australia hope to make it easier to spot the elusive galaxies - using the radio signals fired from their black holes. Astronomers have trained Facebook's facial recognition software to spot'burping' black holes in deep space.